PhD Studentship in Surrey (UK) in collaboration with the European Space Agency

One PhD position is available in Surrey at the Surrey Space Centre of the University of Surrey (UK),

Context
This project builds upon an already established collaboration between the University of Surrey, Surrey Satellite Technology Ltd (SSTL) and the European Space Agency (ESA) where a ground-based automated planning & scheduling system for the imaging campaign of an Earth Observation constellation has been built. Multi agent systems represented a fruitful approach to model this dynamic context. The novelty of this project was to apply Ant Colony Optimisation techniques to achieve optimisation and coordination in the constellation planning. This mechanism offers high-level of adaptability and scalability. The project used mathematical models of non-linear dynamical systems to model the self-organising long-term system’s behaviours. Finally, the system developed has been applied to the ESA GENSO (Global Educational Network for Satellite Operations) network and to the SSTL Disaster Monitoring Constellation.

Project Title
Application of Dynamical Systems Theory to Multi Objective Automated Space Mission Planning based on Ant Colony approach

Project Description
The work proposed here represents the natural step forward. The innovation of this project regards three main research directions:

Multi-Objective Optimisation. The P&S problem can be treated as pareto problem, ie. separate optimisation of objectives to find a set of pareto-optimal solutions (which might have conflicting objective goals). This is becoming a key requirement for goal-oriented planning because extends the way how the objectives can be defined and it could greatly improve the operators’ evaluation of the solutions generated.
Dynamical System Theory. The model previously developed can be extended in a number of directions: the problem representation affects the range of applications of the planning system; the ACO techniques implemented affects its performance.
Hybrid architecture. This research should explore the benefits of hybrid architecture where deliberative agents are set to regulate the collective behaviours expressed by the self-organising multi agent system. This could extend the capabilities and the reliability of the overall system. 

Requirements

The PhD last 3 years. 
The applicant will be based in Surrey at the university. He can spend periods abroad up to 1 year, in general he should spend 6 months in ESOC but it's not mandatory.
The supervisor will be Prof Phil Palmer, head of the group of Astrodynamics at the Surrey Space Centre. Alessandro Donati and Nicola Policella will be his tutor at the European Space Operation Centre (ESOC) of Darmstadt (Germany) and Claudio Iacopino will be his tutor at the Surrey Satellite Technology Ltd (SSTL)
Applicants should hold a good honours undergraduate or Masters degree in computer science or a closely related discipline such as mathematics.
Applicants should have a background in artificial intelligence or advanced computer technologies, ideally automated planning and scheduling and an understanding/experience of the Space Operations/ Earth Observation field.
Applicants' nationality has to be ones of the member states of the European Space Agency

Funding: The applicant will receive full funding for tuition fee, international travels and a salary for 3 years. 

Deadline: Until filled.

Start date: 1st October 2014- 1st January 2015

For information on how to apply and full application requirements please email c.iacopino@sstl.co.uk 

Ph.D. position at the Department of Computer Science of the Missouri University of Science and Technology

Applications are invited for one Ph.D. position at the Department of Computer Science of the Missouri University of Science and Technology. 

Communication networks are an inherent part of our life for a wide range of activities. These networks have increased in size, complexity, heterogeneity, energy demand and interdependence with other networks. These aspects open new research problems, which need to be tackled in order to improve the security and efficiency of these networks. 

The general goal of the Ph.D. work  is the development of efficient solutions for the improvement of security, performance, reliability and energy efficiency of heterogeneous, complex and large scale communication networks, such as geographically distributed web systems, static and mobile wireless sensor networks, small cell networks and wide-area networks. 

The research will be characterized by a theoretical analysis of the problems and proposed solutions, using a wide range of tools from different fields including computational geometry, statistics, algorithm design, optimization and probability theory, as well as by an experimental and simulation study, to investigate and compare the performance of the proposed solutions against the best performing existing approaches. 

We encourage all highly motivated and excellent candidates to apply for this position. 

Applicants must have a bachelor or master degree in Computer Science/Computer Engineering or closely related field, with an emphasis on computer networks. 


Successful candidates should have: 

- Strong background in network algorithm/protocol design and analysis. 
- Strong knowledge of computer networks (wired and wireless). 
- Strong programming skills (e.g. C/C++, Java, Python). 
- Strong skills in oral and written communication. 

For more information please contact Simone Silvestri (simone@cse.psu.edu). 

Application requirements can be found at: 
http://cs.mst.edu/graduatedegreeprograms/phdcomputerscience/

PhD position-Multiview Learning for Sequence Extraction Tasks, Grenoble - Clermont Ferrand, France

Multiview Learning for Sequence Extraction Tasks

Subject:

With the increase of electronically available textual information in different views, new needs for Information Access systems are arising. Many new tasks lie between the classic frameworks of Information Retrieval (IR) and Information Extraction (IE). Machine Learning (ML) is playing a central role in the development of these fields but has been used for the most part for the improvement of existing models. 

The thesis aims at extending the capabilities of statistical IR models to handle more complex information retrieval and extraction tasks. For this, we are interested in the use of probabilistic sequence models for sequence analysis. In particular, we will consider a text generated by two different sources where each of the texts is a sequence of symbols and not as an unordered set. From this perspective, we seek a sequence model that allows to work at a finer level than what is usually done in IR. Each model associated to a source is learned in the way to minimize the disagreement of the sequence to be extracted with the similar extracted sequence by the other model. Under this framework, we aim to deal with several text analysis tasks within a unifying formalism.

Profile:

For this position, we are looking for highly motivated people, with a passion to work in machine learning, information retrieval and the skills to develop algorithms for prediction in real-life applications. We are looking for an inquisitive mind with the curiosity to use a new and challenging technology that requires a rethinking visual processing to achieve a high payoff in terms of speed and efficiency.

We further seek a candidate with the following additional skills:

- Probability and statistics ;

- The ability to analyze, improve and propose new algorithms ;

- Good knowledge of programming languages with a proved experience is a plus.

Application:

The application should include a brief description of research interests and past experience, a CV, degrees and grades, a copy of Master thesis (or a draft thereof), motivation letter (short but pertinent to this call), relevant publications, and other relevant documents. Candidates are encouraged to provide letter(s) of recommendation and contact information to reference persons. Please send your application in one single pdf to 

massih-reza.amini@imag.fr 

eric.gaussier@imag.fr 

gvernat@coffreo.com

The deadline for the application is July 15th, 2014, but we encourage the applicants to contact us as soon as possible. The final decision will be communicated in the beginning of August.

Duration: 3 years (a full time position) Starting date: September, 2014

Supervisors: Massih-Reza Amini (UJF/LIG, France), Eric Gaussier (UJF/LIG, France), Guillaume Vernat (COFFREO, France)

Working Environment:

The PhD candidate will work at AMA team (http://ama.liglab.fr/) of the Laboratoire d'Informatique de Grenoble (LIG) lab, a leading Computer Science in France and COFFREO (https://www.coffreo.com/) a French leading Firm in electronic safe. Grenoble is the capital of the Alps in France, with excellent train connection to Geneva (2h), Paris (3h) and Turin (4h). AMA team is a dynamic group working in Machine Learning and connected scientific domains over 20 researchers (including PhD students) and that covers several aspects of machine learning from theory to applications, including statistical learning, data-mining, and cognitive science.

Benefits:

* Duration 36 months – (3 weeks a month at Grenoble and 1 week a month at Clermont Ferrand)

* Starting date of the contract : September 2014, 

* Salary after taxes: from 21264 € per year,

* Possibility of French courses Help for housing Participation for public transport Scientific Resident card and help for husband/wife visa

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PHD STUDENTS IN THE AREA SOCIAL COMPUTING at Know-Center Graz

PHD STUDENTS IN THE AREA SOCIAL COMPUTING at Know-Center Graz



RESPONSIBILITIES

For a number of research and industry project, we need motivated PhD students in the Area Social Computing, who will have an opportunity to complete a PhD degree in Computer Science at Graz Technical University.



QUALIFICATIONS

* Completed University Diploma/Master's Degree in Computer Science, Software Development and Economy, Telematics or Technical Mathematics (mandatory)

* Prior knowledge in Social Media Analysis, Web Science, Recommender Systems, Social Semantic Web, Social Network Analysis, Science 2.0, Machine Learning, Information Retrieval (highly desirable)

* Experience in object oriented programming in Java, with databases (preferably MySQL, and/or PostgreSQL) (mandatory)

* Experience with frontend technologies (XHTML, JavaScript, CSS, HTML5) (highly desirable)

* Experience with Matlab/Octave, R, Python, PHP, Weka, Apache Solr (highly desirable)

* Team work and self-reliance (mandatory)

* Fluent English (mandatory)



INTEREST IN

* Social Semantic Systems, Analysis of Social Media and Interactions in the Social Web, Social Network Analysis, Recommender Systems, Science 2.0, Web Science, Data Mining, Machine Learning



WE OFFER

* an opportunity to obtain a PhD degree in Computer Science at Graz University of Technology

* young, dynamic, creative team

* informal and stimulating working environment

* on the job training, professional and personal development

* excellent career prospects, both scientific and technical career paths are possible

* compensation of 2,065 per month and up, depending on your education and work experience


We are looking forward to your application. Please email it to us together with relevant attachments at career@know-center.at

--
Dr. Elisabeth Lex

Deputy Head Social Computing Research Group, Know-Center
www.know-center.at

University Assistant
Knowledge Technologies Institute, Graz Univ. of Technology
http://kti.tugraz.at

Tel: 0043 316 873 30841
Fax: 0043 316 873 1030841
Homepage: http://elisabethlex.info

Meet me at i-Know 2014: http://www.i-know.at 

__._,_.___

4 Year PhD Position at the Max-Planck Institute of Cell Biology (MPI-CBG) ,and the Computer Vision Lab Dresden (CVLD)

There is the exciting opportunity to join a rapidly growing team of researcher working in the fields of Computational Biology, Computer Vision and Machine Learning. The PhD will be jointly supervised by Prof. Carsten Rother at CVLD and Dr. Pavel Tomancak at MPI-CBG. The ideal candidate has studied mathematics, physics, computer science or Biology with very good marks. The candidate must be highly motivated and interested to dive into the field of computational biology. It is not necessary, but a plus, if the candidate has a background in biology. The ideal candidate has a strong theoretical background in the field of machine learning, optimization, or computer vision (ideally on structured models), or image processing. It is a big plus if the student has good experience in programming. The exact research topic is still flexible, but in the broad domain of using simulated biological organism to train algorithms that discover high-level biological information, such as cell lineages in long-term, time-lapse microscopic recordings of animal development.


Formal application procedure:
1. You must register with the DIPP (Dresden International PhD Program) here 
http://www.imprs-celldevosys.de/ Latest by 15.7! The PhD applicants will be selected via the normal DIPP hiring procedure. The final application has to be handed in by 25.7
2. Please send your application (CV and letter of motivation) to: carsten.rother@tu-dresden.de. There is no closing date for handing in your application, but please act as early as possible. I will start conducting interviews end of June.
3. The official DIPP interview procedure will take place 15.9-19.9.


Biology in Dresden:
Dresden is one of the top places worldwide in Biology, with different MPIs, TU Dresden (Elite University), and other institutes. Especially the fields of Computational Biology and Bioimaging are rapidly growing. After a successful PhD there are likely many opportunities to continue research in Dresden in these areas. For more information please see: 
http://tu-dresden.de/inf/cvld
http://www.mpi-cbg.de/research/research-groups/pavel-tomancak.html
http://www.mpi-cbg.de/research/research-groups/gene-myers.html
http://compbio-dresden.de/

PhD Studentship, Artificial Intelligence in Mental Health

Artificial Intelligence in Mental Health

Institute of Psychiatry, King's College London

To start: October 2014

 

Award

We are offering one PhD studentship to applicants with a background in Computer Science, particularly Artificial Intelligence or Agent-based Systems. The award is funded by the National Institute for Health Research (NIHR) through the Biomedical Research Centre for Mental Health (BRC-MH) and Biomedical Research Unit for Dementia (BRU-D) at South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry, King’s College London (KCL).

The BRC-MH and BRU-D are pioneering multidisciplinary translational research and experimental medicine in the areas of mental health and neuroscience. They bring together researchers, clinicians and allied health professionals from two internationally recognised organisations. SLaM is the UK’s largest NHS mental health service provider with a long history of involvement in translational research. 

Project

The project aims at evaluating the use of multi-agent computer systems in guiding the clinical decision making process for improved patient care. The project involves designing and implementing a fully workable multi-agent environment responsible for complementing the decision making process. The proposed system will actively monitor entries made to the patient electronic health records through the Electronic Patient Journey System (EPJS) in the aim of anticipating and minimising the occurrence of adverse events, providing evidence-based clinical advice, enforcing adherence to clinical guidelines and capturing and reporting data entry mistakes and clinical errors. The outcome of the project is an active monitoring and guiding system to be incorporated into EPJS, which results less inconsistencies and enforces prompt response to critical events.

Entry requirements

You should have (or be expected to obtain) a 2:1 or 1st class honours degree in Informatics. If you already possess (or expect to obtain) a research-based MSc degree, a merit or distinction level is expected.

Award type and eligibility

The award covers academic fees (Home/EU rate); a tax-free stipend (£16,000 per year) and contributions towards research costs, training and conference attendance for three years. Overseas students and others not meeting UK Home Office residency criteria may apply but would be required to cover the balance of fees over the Home/EU rate.   English language competence criteria apply. 

How to apply

To apply, you must register and complete the application form online using the King's College London Applicationsystem.  Please first of all select “Research Programmes” (the category in which you will find our traditional PhD programme) and then “Social, Genetic & Developmental Psychiatry Research Centre MPhil/PhD (Full-time)” from the drop down list.  In your application, please ensure you enter the reference:  NIHRBRC14 in part 5 of the Funding section. 

Additional documentation can be uploaded after you have submitted your application, but please ensure all supporting documentation, including your references, are uploaded before the interview dates (see below).

We are unable to accept any applications sent directly to the Institute.

Only shortlisted applicants will be contacted.

Closing Date: Thursday 19th June 2014 (23.59 GMT)

Interviews:  9th & 10th July 2014

Background Information

TheInstitute of Psychiatry (IoP), King’s College London has the highest research power of any UK Institution in the area of neuroscience, clinical psychology and psychiatry (2008 Research Assessment Excercise). We are the largest academic community in Europe dedicated to the study, treatment and prevention of mental health problems and neurodegenerative disease. The IoP offers opportunities for research and training in basic and clinical science across the mental health spectrum on one campus. Studying at the IoP you will have access to a large clinical population through King’s Health Partners, an Academic Health Science Centre that includes Guy’s and St Thomas’, King’s College Hospital and South London and Maudsley NHS Foundation Trusts

Postdoctoral Fellowship at Duke-National University of Singapore

Multimodal Neuroimaging in Neuropsychiatric Disorders Laboratory, Center for Cognitive Neuroscience at Duke-NUS Graduate Medical School, National University of Singapore is looking for postdoctoral fellow in cognitive neuroscience and/or multimodal neuroimaging.

Our group studies the human neural bases of social-emotion, cognition, and memory functions and the associated vulnerability patterns in neuropsychiatric disorders, including neurodegenerative diseases (focusing on AD, FTD and preclinical stages) and Schizophrenia. Multimodal neuroimaging and psychophysical techniques are employed, including magnetic resonance imaging (MRI), functional MRI, diffusion tensor imaging, and electroencephalography (EEG). We are interested in examining the network-level structural and functional brain connectivity to shed light on the neurobiological mechanism of disease, paving the way for early detection and intervention.

Candidates must have a passionate enthusiasm for research, a strong background in one of the following fields: cognitive neuroscience, neuropsychiatric disorders, neuroimaging analyses, mathematics/statistics/machine learning or related-fields. He/She should also possess the ability to take the initiative, work independently and be motivated to work in a highly collaborative and international research environment, and be able to demonstrate creativity, technical independence and excellent communication skills. Strong interest in studying social-emotion/cognition/memory functions and/or applications of multimodal neuroimaging in neuropsychiatric disorders is preferred. Proven skills in fMRI/EEG/DTI data analyses is a plus but not necessary.

Key attractions are access to a 3T Tim Trio MR scanner and a MR compatible digital EEG system as well as collaboration opportunities with an excellent network of domestic and international scientists and doctors. The position will be two years with possible extension. Competitive package will be provided based on experience.

Interested applicants are welcome to email Assistant Prof. Helen Juan Zhou at helen.zhou@duke-nus.edu.sg with application letter, curriculum vitae, three references, and contact information. Website: https://sites.google.com/site/mneuroimaginglab/; http://www.duke-nus.edu.sg/content/zhou-juan-helen